6 accepted papers
Multi-objective reinforcement learning (MORL) is a fundamental framework for real-world decision-making problems involving multiple conflicting criteria. Existing multi-policy (MP) methods typically rely on online evolutionary frameworks that maintain large policy populations, leading to high sample…
Emotion recognition from speech and music shares similarities due to their acoustic overlap, which has led to interest in transferring knowledge between these domains. However, the shared acoustic cues between speech and music, particularly those encoded by Self-Supervised Learning (SSL) models, rem…
Some End-to-End (E2E) Automatic Speech Recognition (ASR) models, such as Attention-based Encoder-Decoder (AED) and Recurrent Neural Network Transducer (RNN-T) are known to have components that effectively act as internal language models (ILM), implicitly modelling the prior probability of the output…
Gesture synthesis has gained significant attention as a critical research field, aiming to produce contextually appropriate and natural gestures corresponding to speech or textual input. Although deep learning-based approaches have achieved remarkable progress, they often overlook the rich semantic
End-to-end Automatic Speech Recognition (E2E ASR) significantly simplifies the training process of an ASR model. Connectionist Temporal Classification (CTC) is one of the most popular methods for E2E ASR training. Implicitly, CTC has a unique topology which is very useful for sequence modelling. How…
In recent years, the vulnerability of networks has attracted the attention of researchers. However, in these methods, the impact of video compression coding on the added adversarial perturbation, i.e., the robustness of the video adversarial example, is not considered. When an adversarial sample is…